We use Multiple Discriminant Analysis (MDA) to compare the predictive abilities of these models. This paper aims to predict companies financial distress situation with the use of four different models Altman Z score, Revised Altman Z Score (Linear. Abstract: - Purpose of this study is to determine whether cash flow impacts business failure prediction using the BP models (Altman z-score, or Neural Network, or any of the BP models which could be implemented having objective to predict the financial distress or more complex financial failure-bankruptcy of the banks or companies). While Altman’s research focuses on manufacturing companies, for the purpose of this study, we selected firms from five sectors energy, consumer discretionary, consumer staples, industrials and materials. Kamau (2007) developed a failure prediction model using cash flow information and multiple discriminant analysis techniques.
Springate model (S-Score) has the highest level of accuracy in predicting financial distress, which is 68.75. The result of this study showed predictor variable that gave discriminating power which stood of quality of earning. By testing and comparing these models using a sample of 70 bankrupt firms against a population sample of 1,047 non-distressed firms, this study determines which models has a higher discriminating ability. The results of this study indicate that the. Secondly, we add Cash Flow from Operation (CFO) to Total Liabilities (TL) ratio as a sixth variable to improve Altman’s (1968) standard Z score model.
First, we estimate Altman’s original model and test the efficiency of the cut-off region, coefficients and the variables used. The purpose of this study is to assess the effectiveness of Altman’s Z score in predicting corporate bankruptcy for Canadian listed companies.
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